The present invention provides a method of a multi-parameter self learning machine application model. By monitoring voltages and currents, this method is capable of creating multiple self-learning user controllable multi-parameter models which describe the power condition, load and motor (or any combination thereof).
The invention further provides a self-learning diagnostic technique, whereby electrical signals (voltages and currents) are monitored, combined in various ways and analyzed. The purpose is to visualize differences in patterns of behavior of the machine-driven system (power supply, motor and load); such that problems relating to these three components (or any combination thereof) can be more easily identified and analyzed.
The invention allows root cause analysis and early forecasting of incipient failure modes by moving through a complicated set of interconnected equations to identify the value that is causing the most change in the system. The area of diagnostics is directed to 3 phase electrical machines. Currents and voltages are gathered and then applied to the inventive method. The method utilizes several “signals” and “learns” the “typical” behavior of the system.